How Uber Uses Transformer
13 engineering articles about Transformer from Uber's engineering team
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This article discusses the development and implementation of forecasting models aimed at improving driver availability at airports, which are critical to Uber's ridesharing ecosystem.
Bob Zheng, Dhruv Ghulati, Manoj Panikkar, Michael (Yichuan) Cai
15 min read
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The article discusses DragonCrawl, a generative AI system developed by Uber to enhance mobile testing by mimicking human-like interactions with applications.
Juan Marcano, Mengdie Zhang, Ali Zamani, Anam Hira
18 min read
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The article discusses DeepETA, Uber's advanced model for predicting arrival times using deep learning techniques.
ApacheApache SparkComputer VisionDeep LearningMachine LearningSelf-AttentionTensorFlowTransformerTransformersXGBoost
Xinyu Hu, Olcay Cirit, Tanmay Binaykiya, Ramit Hora
15 min read
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The article discusses the integration of Elastic Distributed Training with XGBoost on Ray, highlighting how this approach addresses challenges in distributed machine learning at scale.
Michael Mui, Xu Ning, Kai Fricke, Amog Kamsetty, Richard Liaw
19 min read
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Ludwig version 0. 3 introduces significant enhancements, including hyperparameter optimization, support for Transformers, and integration with TensorFlow 2.
Kerri Brown, Piero Molino, Yaroslav Dudin
10 min read
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The article discusses the Plug and Play Language Model (PPLM), a novel approach to controlled text generation that allows users to steer large, pre-trained language models without the need for retr...
Rosanne Liu, Sumanth Dathathri, Andrea Madotto, Piero Molino, Jason Yosinski
19 min read
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The article discusses the evolution of the Michelangelo model representation at Uber to enhance flexibility and scalability in machine learning model serving.
Anne Holler, Michael Mui
15 min read
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Ludwig v0. 2 introduces significant enhancements to its deep learning toolbox, including new features such as Comet.
Piero Molino, Yaroslav Dudin, Sai Sumanth Miryala
10 min read
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The article discusses the latest updates to Horovod, a distributed deep learning framework, which now includes support for PySpark and Apache MXNet, along with features aimed at enhancing training ...
Carsten Jacobsen
7 min read
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The article introduces Ludwig, an open-source deep learning toolbox developed by Uber that allows users to train and test deep learning models without writing code.
Piero Molino, Yaroslav Dudin, Sai Sumanth Miryala
13 min read
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The article details the author's experience as a summer intern at Uber, specifically focusing on the development of the Uber Eats Menu Scheduler.
Jonathan Levi
10 min read
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The article discusses the evolution and scaling of Uber's machine learning platform, Michelangelo, highlighting its development, deployment, and operational strategies.
Jeremy Hermann, Mike Del Balso
29 min read
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The article discusses the JVM Profiler, an open-source tool developed by Uber for tracing distributed JVM applications at scale.
Bo Yang, Nan Zhu, Felix Cheung, Xu Ning
9 min read
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